The Teager Energy Based Features for Identification of Identical Twins in Multi-lingual Environment
Automatic Speaker Recognition (ASR) is an economic method of biometrics because of the availability of low cost and powerful processors. An important question which must be answered for the ASR system is how well the system resists the effects of determined mimics such as those based on physiological characteristics especially identical twins or triplets. In this paper, a new feature set based on Teager Energy Operator (TEO) and well-known Mel frequency cepstral coefficients (MFCC) is developed. The effectiveness of the newly derived feature set in identifying identical twins has been demonstrated for different Indian languages. Polynomial classifiers of 2nd and 3rd order have been used. The results have been compared with other feature sets such as LPC coefficients, LPC cepstrum and baseline MFCC.
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